Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| from transformers import ( | |
| pipeline, | |
| AutoModelForSequenceClassification, | |
| AutoTokenizer | |
| ) | |
| import torch | |
| import re | |
| # ===== CONSTANTS ===== | |
| MAX_CHARS = 1500 # Increased character limit | |
| SUPPORTED_LANGUAGES = { | |
| 'en': 'English', | |
| 'zh': 'Chinese', | |
| 'yue': 'Cantonese', | |
| 'ja': 'Japanese', | |
| 'ko': 'Korean' | |
| } | |
| # ===== ASPECT CONFIGURATION ===== | |
| aspect_map = { | |
| # Location related | |
| "location": ["location", "near", "close", "access", "transport", "distance", "area", "tsim sha tsui", "kowloon"], | |
| "view": ["view", "scenery", "vista", "panorama", "outlook", "skyline"], | |
| "parking": ["parking", "valet", "garage", "car park", "vehicle"], | |
| # Room related | |
| "room comfort": ["comfortable", "bed", "pillows", "mattress", "linens", "cozy", "hard", "soft"], | |
| "room cleanliness": ["clean", "dirty", "spotless", "stains", "hygiene", "sanitation", "dusty"], | |
| "room amenities": ["amenities", "minibar", "coffee", "tea", "fridge", "facilities", "tv", "kettle"], | |
| "bathroom": ["bathroom", "shower", "toilet", "sink", "towel", "faucet", "toiletries"], | |
| # Service related | |
| "staff service": ["staff", "friendly", "helpful", "rude", "welcoming", "employee", "manager"], | |
| "reception": ["reception", "check-in", "check-out", "front desk", "welcome", "registration"], | |
| "housekeeping": ["housekeeping", "maid", "cleaning", "towels", "service", "turndown"], | |
| "concierge": ["concierge", "recommendation", "advice", "tips", "guidance", "directions"], | |
| "room service": ["room service", "food delivery", "order", "meal", "tray"], | |
| # Facilities | |
| "dining": ["breakfast", "dinner", "restaurant", "meal", "food", "buffet", "lunch"], | |
| "bar": ["bar", "drinks", "cocktail", "wine", "lounge", "happy hour"], | |
| "pool": ["pool", "swimming", "jacuzzi", "sun lounger", "deck", "towels"], | |
| "spa": ["spa", "massage", "treatment", "relax", "wellness", "sauna"], | |
| "fitness": ["gym", "fitness", "exercise", "workout", "training", "weights"], | |
| # Technical | |
| "Wi-Fi": ["wifi", "internet", "connection", "online", "network", "speed"], | |
| "AC": ["air conditioning", "AC", "temperature", "heating", "cooling", "ventilation"], | |
| "elevator": ["elevator", "lift", "escalator", "vertical transport", "wait"], | |
| # Value | |
| "pricing": ["price", "expensive", "cheap", "value", "rate", "cost", "worth"], | |
| "extra charges": ["charge", "fee", "bill", "surcharge", "additional", "hidden"] | |
| } | |
| aspect_responses = { | |
| "location": "We're delighted you enjoyed our prime location in the heart of Tsim Sha Tsui, with convenient access to Nathan Road shopping and the Star Ferry pier.", | |
| "view": "It's wonderful to hear you appreciated the beautiful harbor or city skyline views from your room.", | |
| "room comfort": "Our housekeeping team takes special care with our pillow menu and mattress toppers to ensure your comfort.", | |
| "room cleanliness": "Your commendation of our cleanliness standards means a lot to our dedicated housekeeping staff.", | |
| "staff service": "Your kind words about our team, especially {staff_name}, have been shared with them - such recognition means everything to us.", | |
| "reception": "We're pleased our front desk team made your arrival and departure experience seamless.", | |
| "spa": "Our award-winning spa therapists will be delighted you enjoyed their signature treatments.", | |
| "pool": "We're glad you had a refreshing time at our rooftop pool with its stunning city views.", | |
| "dining": "Thank you for appreciating our culinary offerings at The Burgeroom and Chinese Restaurant - we've shared your feedback with Executive Chef Wong.", | |
| "concierge": "We're happy our concierge team could enhance your stay with their local expertise and recommendations.", | |
| "fitness": "It's great to hear you made use of our 24-hour fitness center with its panoramic views.", | |
| "room service": "We're pleased our 24-hour in-room dining met your expectations for both quality and timeliness.", | |
| "parking": "We're glad our convenient valet parking service made your arrival experience hassle-free.", | |
| "bathroom": "Our housekeeping team takes special pride in maintaining our marble bathrooms with premium amenities." | |
| } | |
| improvement_actions = { | |
| "AC": "completed a comprehensive inspection and maintenance of all air conditioning units", | |
| "housekeeping": "implemented additional training for our housekeeping team and revised cleaning schedules", | |
| "bathroom": "conducted deep cleaning of all bathrooms and replenished premium toiletries", | |
| "parking": "introduced new digital key management with our valet service to reduce wait times", | |
| "dining": "reviewed all menu pricing and quality standards with our culinary leadership team", | |
| "reception": "provided enhanced customer service training focused on cultural sensitivity", | |
| "elevator": "performed full servicing of all elevators and adjusted peak-time scheduling", | |
| "room amenities": "begun upgrading in-room amenities including new coffee machines and smart TVs", | |
| "Wi-Fi": "upgraded our network infrastructure to provide faster and more reliable internet", | |
| "noise": "initiated soundproofing improvements in corridors and between rooms", | |
| "pricing": "started a comprehensive review of our pricing structure and value proposition", | |
| "room service": "revised our in-room dining operations to improve delivery times", | |
| "view": "scheduled window cleaning and tree trimming to maintain optimal views", | |
| "fitness": "upgraded gym equipment based on guest feedback about variety" | |
| } | |
| # ===== MODEL LOADING ===== | |
| def load_sentiment_model(): | |
| model = AutoModelForSequenceClassification.from_pretrained("smtsead/fine_tuned_bertweet_hotel") | |
| tokenizer = AutoTokenizer.from_pretrained('finiteautomata/bertweet-base-sentiment-analysis') | |
| return model, tokenizer | |
| def load_aspect_classifier(): | |
| return pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33") | |
| # ===== CORE FUNCTIONS ===== | |
| def analyze_sentiment(text, model, tokenizer): | |
| inputs = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='pt') | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probs = torch.nn.functional.softmax(outputs.logits, dim=-1) | |
| predicted_label = torch.argmax(probs).item() | |
| confidence = torch.max(probs).item() | |
| return { | |
| 'label': predicted_label, | |
| 'confidence': f"{confidence:.0%}", | |
| 'sentiment': 'POSITIVE' if predicted_label else 'NEGATIVE' | |
| } | |
| def detect_aspects(text, aspect_classifier): | |
| relevant_aspects = [] | |
| text_lower = text.lower() | |
| for aspect, keywords in aspect_map.items(): | |
| if any(re.search(rf'\b{kw}\b', text_lower) for kw in keywords): | |
| relevant_aspects.append(aspect) | |
| if relevant_aspects: | |
| result = aspect_classifier( | |
| text, | |
| candidate_labels=relevant_aspects, | |
| multi_label=True, | |
| hypothesis_template="This review discusses the hotel's {}." | |
| ) | |
| return [(aspect, f"{score:.0%}") for aspect, score in | |
| zip(result['labels'], result['scores']) if score > 0.6] | |
| return [] | |
| def generate_response(sentiment, aspects, original_text): | |
| # Personalization | |
| guest_name = "" | |
| name_match = re.search(r"(Mr\.|Ms\.|Mrs\.)\s(\w+)", original_text, re.IGNORECASE) | |
| if name_match: | |
| guest_name = f" {name_match.group(2)}" | |
| # Staff name extraction | |
| staff_name = "" | |
| staff_match = re.search(r"(receptionist|manager|concierge|chef)\s(\w+)", original_text, re.IGNORECASE) | |
| if staff_match: | |
| staff_name = staff_match.group(2) | |
| if sentiment['label'] == 1: | |
| response = f"""Dear{guest_name if guest_name else ' Valued Guest'}, | |
| Thank you for choosing The Kimberley Hotel Hong Kong and for sharing your wonderful feedback!""" | |
| # Add relevant aspect responses | |
| added_aspects = set() | |
| for aspect, _ in aspects: | |
| if aspect in aspect_responses: | |
| response_text = aspect_responses[aspect] | |
| if "{staff_name}" in response_text and staff_name: | |
| response_text = response_text.format(staff_name=staff_name) | |
| response += "\n\n" + response_text | |
| added_aspects.add(aspect) | |
| if len(added_aspects) >= 3: # Limit to 3 main points | |
| break | |
| # Special offers | |
| if "room" in added_aspects or "dining" in added_aspects: | |
| response += "\n\nAs a token of our appreciation, we'd like to offer you a complimentary room upgrade or dining credit on your next stay. Simply mention code VIP2024 when booking." | |
| response += "\n\nWe look forward to welcoming you back to your home in Hong Kong!\n\nWarm regards," | |
| else: | |
| response = f"""Dear{guest_name if guest_name else ' Guest'}, | |
| Thank you for your valuable feedback - we sincerely apologize that your experience didn't meet our usual high standards.""" | |
| # Add improvement actions | |
| added_improvements = set() | |
| for aspect, _ in aspects: | |
| if aspect in improvement_actions: | |
| response += f"\n\nRegarding your comments about the {aspect}, we've {improvement_actions[aspect]}." | |
| added_improvements.add(aspect) | |
| if len(added_improvements) >= 2: # Limit to 2 main improvements | |
| break | |
| # Recovery offer | |
| recovery_offer = "\n\nTo make amends, we'd like to offer you:" | |
| if "room" in added_improvements: | |
| recovery_offer += "\n- One night complimentary room upgrade" | |
| if "dining" in added_improvements: | |
| recovery_offer += "\n- HKD 300 dining credit at our restaurants" | |
| if not ("room" in added_improvements or "dining" in added_improvements): | |
| recovery_offer += "\n- 15% discount on your next stay" | |
| response += recovery_offer | |
| response += "\n\nPlease contact our Guest Relations Manager Ms. Chan directly at [email protected] to arrange this." | |
| response += "\n\nWe hope for another opportunity to provide you with the exceptional experience we're known for.\n\nSincerely," | |
| return response + "\nMichael Wong\nGuest Experience Manager\nThe Kimberley Hotel Hong Kong\n+852 1234 5678" | |
| # ===== STREAMLIT UI ===== | |
| def main(): | |
| # Page Config | |
| st.set_page_config( | |
| page_title="Kimberley Review Assistant", | |
| page_icon="🏨", | |
| layout="centered" | |
| ) | |
| # Custom CSS | |
| st.markdown(""" | |
| <style> | |
| .header { | |
| color: #003366; | |
| font-size: 28px; | |
| font-weight: bold; | |
| margin-bottom: 10px; | |
| } | |
| .subheader { | |
| color: #666666; | |
| font-size: 16px; | |
| margin-bottom: 30px; | |
| } | |
| .badge { | |
| background-color: #e6f2ff; | |
| color: #003366; | |
| padding: 3px 10px; | |
| border-radius: 15px; | |
| font-size: 14px; | |
| display: inline-block; | |
| margin: 0 5px 5px 0; | |
| } | |
| .char-counter { | |
| font-size: 12px; | |
| color: #666; | |
| text-align: right; | |
| margin-top: -15px; | |
| margin-bottom: 15px; | |
| } | |
| .char-counter.warning { | |
| color: #ff6b6b; | |
| } | |
| .result-box { | |
| border-left: 4px solid #003366; | |
| padding: 15px; | |
| background-color: #f9f9f9; | |
| margin: 20px 0; | |
| border-radius: 0 8px 8px 0; | |
| white-space: pre-wrap; | |
| } | |
| .aspect-badge { | |
| background-color: #e6f2ff; | |
| color: #003366; | |
| padding: 2px 8px; | |
| border-radius: 4px; | |
| font-size: 14px; | |
| display: inline-block; | |
| margin: 2px; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Header | |
| st.markdown('<div class="header">The Kimberley Hotel Hong Kong</div>', unsafe_allow_html=True) | |
| st.markdown('<div class="subheader">Guest Review Analysis System</div>', unsafe_allow_html=True) | |
| # Supported Languages | |
| st.markdown("**Supported Review Languages:**") | |
| lang_cols = st.columns(5) | |
| for i, (code, name) in enumerate(SUPPORTED_LANGUAGES.items()): | |
| lang_cols[i].markdown(f'<div class="badge">{name}</div>', unsafe_allow_html=True) | |
| # Review Input with Character Counter | |
| review = st.text_area("**Paste Guest Review:**", | |
| height=250, | |
| max_chars=MAX_CHARS, | |
| placeholder=f"Enter review in any supported language (max {MAX_CHARS} characters)...", | |
| key="review_input") | |
| char_count = len(st.session_state.review_input) if 'review_input' in st.session_state else 0 | |
| char_class = "warning" if char_count > MAX_CHARS else "" | |
| st.markdown(f'<div class="char-counter {char_class}">{char_count}/{MAX_CHARS} characters</div>', | |
| unsafe_allow_html=True) | |
| if st.button("Analyze & Generate Response", type="primary"): | |
| if not review.strip(): | |
| st.error("Please enter a review") | |
| return | |
| if char_count > MAX_CHARS: | |
| st.warning(f"Review truncated to {MAX_CHARS} characters for analysis") | |
| review = review[:MAX_CHARS] | |
| with st.spinner("Analyzing feedback..."): | |
| # Load models | |
| sentiment_model, tokenizer = load_sentiment_model() | |
| aspect_classifier = load_aspect_classifier() | |
| # Process review | |
| sentiment = analyze_sentiment(review, sentiment_model, tokenizer) | |
| aspects = detect_aspects(review, aspect_classifier) | |
| response = generate_response(sentiment, aspects, review) | |
| # Display results | |
| st.divider() | |
| # Sentiment and Aspects | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("### Sentiment Analysis") | |
| sentiment_icon = "✅" if sentiment['label'] == 1 else "⚠️" | |
| st.markdown(f"{sentiment_icon} **{sentiment['sentiment']}**") | |
| st.caption(f"Confidence level: {sentiment['confidence']}") | |
| with col2: | |
| st.markdown("### Key Aspects Detected") | |
| if aspects: | |
| for aspect, score in sorted(aspects, key=lambda x: float(x[1][:-1]), reverse=True): | |
| st.markdown(f'<div class="aspect-badge">{aspect} ({score})</div>', unsafe_allow_html=True) | |
| else: | |
| st.markdown("_No specific aspects detected_") | |
| # Generated Response | |
| st.divider() | |
| st.markdown("### Draft Response") | |
| st.markdown(f'<div class="result-box">{response}</div>', unsafe_allow_html=True) | |
| # Copy button | |
| if st.button("Copy Response to Clipboard"): | |
| st.session_state.copied = True | |
| st.rerun() | |
| if st.session_state.get("copied", False): | |
| st.success("Response copied to clipboard!") | |
| st.session_state.copied = False | |
| if __name__ == "__main__": | |
| main() |